ReviewAnnual reviews in control2021
Structural identifiability and observability of compartmental models of the COVID-19 pandemic.
Review in Annual reviews in control, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Article
- Parameter identifiability of linear-compartmental mammillary models.Bulletin of mathematical biology · 2025Article
- Structural and practical identifiability of within-host models of virus dynamics - a review.Current opinion in systems biology · 2025Article
- Mathematical models to characterize the early phase of the COVID-19 pandemic in New Mexico, USA.Mathematical biosciences and engineering : MBE · 2025Article
- Article
- Structural and Practical Identifiability of Phenomenological Growth Models for Epidemic Forecasting.Viruses · 2025Article
- A Bayesian model calibration framework for stochastic compartmental models with both time-varying and time-invariant parameters.Infectious Disease Modelling · 2024Article
- Global and local identifiability analysis of a nonlinear biphasic constitutive model in confined compression.Journal of the Royal Society, Interface · 2024Article
- Structural Identifiability and Observability of Microbial Community Models.Bioengineering (Basel, Switzerland) · 2023Article
- On Parameter Identifiability in Network-Based Epidemic Models.Bulletin of mathematical biology · 2023Article
- Article
- Multi-objective T-S fuzzy control of Covid-19 spread model: An LMI approach.Biomedical signal processing and control · 2023Article
- The Transmission Dynamics of a Compartmental Epidemic Model for COVID-19 with the Asymptomatic Population via Closed-Form Solutions.Vaccines · 2022Article
- The Structural Identifiability of a Humidity-Driven Epidemiological Model of Influenza Transmission.Viruses · 2022Article
- Structural identifiability of compartmental models for infectious disease transmission is influenced by data type.Epidemics · 2022Article
- Why the Spectral Radius? An intuition-building introduction to the basic reproduction number.Bulletin of mathematical biology · 2022Article
- Modeling pandemic to endemic patterns of SARS-CoV-2 transmission using parameters estimated from animal model data.PNAS nexus · 2022Article
- Non-Linear Observer Design with Laguerre Polynomials.Entropy (Basel, Switzerland) · 2022Article
- Impacts of a delayed and slow-paced vaccination on cases and deaths during the COVID-19 pandemic: a modelling study.Journal of the Royal Society, Interface · 2022Article
- Framework for enhancing the estimation of model parameters for data with a high level of uncertainty.Nonlinear dynamics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent coronavirus disease (COVID-19) outbreak has dramatically increased the public awareness and appreciation of the utility of dynamic models. At the same time, the dissemination of contradictory model predictions has highlighted their limitations. If some parameters and/or state variables of a model cannot be determined from output measurements, its ability to yield correct insights - as well as the possibility of controlling the system - may be compromised. Epidemic dynamics are commonly analysed using compartmental models, and many variations of such models have been used for analysing and predicting the evolution of the COVID-19 pandemic. In this paper we survey the different models proposed in the literature, assembling a list of 36 model structures and assessing their ability to provide reliable information. We address the problem using the control theoretic concepts of structural identifiability and observability. Since some parameters can vary during the course of an epidemic, we consider both the constant and time-varying parameter assumptions. We analyse the structural identifiability and observability of all of the models, considering all plausible choices of outputs and time-varying parameters, which leads us to analyse 255 different model versions. We classify the models according to their structural identifiability and observability under the different assumptions and discuss the implications of the results. We also illustrate with an example several alternative ways of remedying the lack of observability of a model. Our analyses provide guidelines for choosing the most informative model for each purpose, taking into account the available knowledge and measurements.
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